PhD in Machine Learning

Project Overview/nIterative RANS-based CFD design is approaching its practical limits. While high-fidelity simulation remains essential, the repeated geometry-CFD-evaluation loop that dominates turbomachinery design is increasingly the primary bottleneck. This PhD aims to fundamentally change the turbomachinery design process by replacing iterative RANS-based CFD loops with regression-driven inverse design. The research will develop models that map aerodynamic requirements directly to 3D blade geometries and associated flow fields, and extend a proven 2D inverse design framework to full 3D turbomachinery configurations. The ultimate goal is to retain the fidelity and robustness of RANS-based design while reducing design iteration times by over 90%. Location: Cambridge.